S
S.A. Karkanis
Researcher at American Hotel & Lodging Educational Institute
Publications - 49
Citations - 1418
S.A. Karkanis is an academic researcher from American Hotel & Lodging Educational Institute. The author has contributed to research in topics: Wavelet & Feature extraction. The author has an hindex of 18, co-authored 47 publications receiving 1289 citations. Previous affiliations of S.A. Karkanis include Hitec & National and Kapodistrian University of Athens.
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Journal ArticleDOI
Computer-aided tumor detection in endoscopic video using color wavelet features
TL;DR: An approach to the detection of tumors in colonoscopic video based on a new color feature extraction scheme to represent the different regions in the frame sequence based on the wavelet decomposition, reaching 97% specificity and 90% sensitivity.
Journal ArticleDOI
An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy.
TL;DR: A novel system for the support of the detection of adenomas in gastrointestinal video endoscopy that accepts standard low-resolution video input thus requiring less computational resources and facilitating both portability and the potential to be used in telemedicine applications is presented.
Journal ArticleDOI
CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames
TL;DR: In this paper, the authors presented CoLD (colorectal lesions detector) an innovative detection system to support colorect cancer diagnosis and detection of pre-cancerous polyps, by processing endoscopy images or video frame sequences acquired during colonoscopy.
Proceedings ArticleDOI
A comparative study of texture features for the discrimination of gastric polyps in endoscopic video
TL;DR: The results advocate the feasibility of a computer-based system for polyp detection in video gastroscopy that exploits the textural characteristics of the gastric mucosa in conjunction with its color appearance.
Journal ArticleDOI
Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images
Dimitris Maroulis,Michalis A. Savelonas,Dimitris K. Iakovidis,S.A. Karkanis,N. Dimitropoulos +4 more
TL;DR: From the quantification of the results, two major impacts have been derived: higher average accuracy in the delineation of hypoechoic thyroid nodules, which exceeds 91%; and faster convergence when compared with the ACWE model.